Literature DB >> 12460461

Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma.

Tatsuya Ando1, Miyuki Suguro, Taizo Hanai, Takeshi Kobayashi, Hiroyuki Honda, Masao Seto.   

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long-overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes (CD10, AA807551, AA805611 and IRF-4) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy.

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Year:  2002        PMID： 12460461      PMCID： PMC5926895          DOI： 10.1111/j.1349-7006.2002.tb01225.x

Source DB:  PubMed          Journal:  Jpn J Cancer Res        ISSN： 0910-5050


  20 in total

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Journal:  Leukemia       Date:  1999-09       Impact factor: 11.528

2.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.

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Journal:  Nat Med       Date:  2002-01       Impact factor: 53.440

3.  Deregulation of MUM1/IRF4 by chromosomal translocation in multiple myeloma.

Authors:  S Iida; P H Rao; M Butler; P Corradini; M Boccadoro; B Klein; R S Chaganti; R Dalla-Favera
Journal:  Nat Genet       Date:  1997-10       Impact factor: 38.330

4.  Fuzzy neural network-based prediction of the motif for MHC class II binding peptides.

Authors:  H Noguchi; T Hanai; H Honda; L C Harrison; T Kobayashi
Journal:  J Biosci Bioeng       Date:  2001       Impact factor: 2.894

5.  De novo CD5+ diffuse large B-cell lymphoma: a clinicopathologic study of 109 patients.

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Journal:  Nature       Date:  2002-01-31       Impact factor: 49.962

7.  Molecular cloning of the common acute lymphoblastic leukemia antigen (CALLA) identifies a type II integral membrane protein.

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8.  Relationship of p53, bcl-2, and tumor proliferation to clinical drug resistance in non-Hodgkin's lymphomas.

Authors:  W H Wilson; J Teruya-Feldstein; T Fest; C Harris; S M Steinberg; E S Jaffe; M Raffeld
Journal:  Blood       Date:  1997-01-15       Impact factor: 22.113

9.  A predictive model for aggressive non-Hodgkin's lymphoma.

Authors: 
Journal:  N Engl J Med       Date:  1993-09-30       Impact factor: 91.245

10.  CD10+ cell population in the bone marrow of patients with advanced neuroblastoma.

Authors:  M Mandel; G Rechavi; Y Neumann; M Biniaminov; E Rosenthal; A Toren; F Brok-Simoni; I Ben-Bassat; B Ramot
Journal:  Med Pediatr Oncol       Date:  1994
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  9 in total

1.  Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method.

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Journal:  BMC Bioinformatics       Date:  2006-09-04       Impact factor: 3.169

2.  A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification.

Authors:  Manju R Mamtani; Tushar P Thakre; Mrunal Y Kalkonde; Manik A Amin; Yogeshwar V Kalkonde; Amit P Amin; Hemant Kulkarni
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3.  Analysis of gene expression profiles of soft tissue sarcoma using a combination of knowledge-based filtering with integration of multiple statistics.

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Journal:  PLoS One       Date:  2014-09-04       Impact factor: 3.240

Review 4.  Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review.

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5.  Applications of machine learning in cancer prediction and prognosis.

Authors:  Joseph A Cruz; David S Wishart
Journal:  Cancer Inform       Date:  2007-02-11

6.  Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data.

Authors:  Yasunori Ushida; Ryuji Kato; Kosuke Niwa; Daisuke Tanimura; Hideo Izawa; Kenji Yasui; Tomokazu Takase; Yasuko Yoshida; Mitsuo Kawase; Tsutomu Yoshida; Toyoaki Murohara; Hiroyuki Honda
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8.  Application of independent component analysis to microarrays.

Authors:  Su-In Lee; Serafim Batzoglou
Journal:  Genome Biol       Date:  2003-10-24       Impact factor: 13.583

9.  Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

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  9 in total

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